Predictive IDH Genotyping Based on the Evaluation of Spatial Metabolic Heterogeneity by Compartmental Uptake Characteristics in Preoperative Glioma Using 18F-FET PET

Visual Abstract

Gl iomas are the most common primary malignant neoplasms of the central nervous system (CNS) in adults and comprise a large spectrum of molecular subtypes with intricate pathophysiology.Molecular stratification is essential for diagnosis, treatment planning, and individual prognosis.In recent years, the World Health Organization (WHO) classification of tumors of the CNS has undergone several updates (1) introducing several important changes, such as the incorporation of molecular and genetic information.One important molecular marker that has gained significant attention is the mutation status of the isocitrate dehydrogenase (IDH) gene (2)(3)(4).IDH gene mutations play a central role in glioma pathophysiology, occurring early in the glioma genesis and characterizing a group of tumors that is molecularly distinct from primary glioblastoma.Because IDH mutations are associated with a more favorable prognosis (5), the IDH genotype has become a central feature in the diagnosis and management of patients with CNS cancer.In recent years, considerable progress in understanding the molecular mechanisms and pathophysiology underlying IDH mutations has been made.IDH genes encode a key enzyme in the tricarboxylic acid cycle, which is a central cellular pathway for energy production.When IDH genes are altered, a profound disruption in the tricarboxylic acid cycle with dysregulation of the amino acid metabolism is induced-a hallmark of CNS cancer-which is leveraged for bioenergetic processes and protein synthesis.By a gain-of-function mutation, the physiologic conversion of isocitrate to a-ketoglutarate, an important intermediate metabolite in the Krebs cycle, is inhibited, whereas the production of D-2-hydroxyglutarate is propagated.High levels of the oncometabolite D-2-hydroxyglutarate mediate global DNA and histone hypermethylation, impairment of DNA break repair mechanisms, and a decrease in hypoxia-inducible factors through competitive inhibition of tumor suppressors in the a-ketoglutarate-dependent dioxygenase family that contribute to glioma pathogenesis and progression through alteration of cellular differentiation, proliferation, and gene expression (2,3).
In the surgical management of patients with preoperative IDHmutated glioma, supramaximal resection was shown to improve overall survival (5)(6)(7)(8).However, the IDH genotype is typically unknown before surgery, and a preceding (stereotactic or open) biopsy involves the hazards of perioperative complications.To date, there are few reliable means for noninvasive and predictive genotyping of IDH mutation status in clinical practice (5).
On the basis of the hypothesis that specific genetic alterations are linked with distinct metabolic phenotypes, we introduced the compartmental uptake (CU) ratio as a noninvasive metabolic imaging biomarker characterizing the spatially heterogeneous glioma metabolism by differentiating the metabolic tumor core from its periphery.Predictive genotyping of IDH mutation status was then investigated using O-(2-18 F-fluoroethyl)-L-tyrosine (FET) PET, as an established marker for amino acid metabolism, in a patient cohort with preoperative glioma.

Study Design and Patients
This retrospective clinical cohort study was conducted according to the principles of the Helsinki Declaration.Approval from the institutional ethics board was obtained (EA2/019/23).Informed consent was obtained from all participants.From 200 consecutive suspected-glioma patients evaluated using a simultaneous 18 F-FET PET/MRI approach between 2017 and 2022, 52 participants with hybrid imaging before resection (Fig. 1) were included according to the eligibility criteria.

Neuropathologic Analysis
The molecular status of IDH mutation (IDH-mutated or wild-type), 1p/19q codeletion (loss of heterozygosity of chromosomes 1p and 19q [LOH1p/19q]-positive, codeleted; LOH1p/19q-negative, nondeleted), MGMT promoter methylation (MGMT-positive, methylated; MGMTnegative, unmethylated), and ATRX loss (ATRX-positive, deficient; ATRX-negative, retention) were determined from formalin-fixed paraffinembedded tissue specimens during routine diagnostic workup procedures using fluorescence in situ hybridization analysis, pyrosequencing, EPIC DNA methylation arrays (Illumina), or immunostainings according to the requirements of the WHO classification of tumors of the CNS (1).When pyrosequencing of MGMT promoter methylation was used, a cutoff of 10% was defined to classify MGMT methylated versus unmethylated cases, a cutoff that is commonly applied and has been validated for routine clinical diagnostics (9).Gliomas were classified using the 2021 WHO classification (1) according to the molecular data available at that time point.

PET/MRI Acquisition
Simultaneous PET/MRI was performed on a Magnetom Biograph mMR scanner (Siemens Healthcare) with an averaged axial spatial resolution of 6 mm in full width at half maximum, which was determined in a 3-dimensional Hoffman brain phantom measurement (ordered-subsets expectation maximization, 3 iterations and 21 subsets, postfiltering by a 3-dimensional gaussian kernel of 3 mm in full width at half maximum, as in patient data) following the method by Joshi et al. (10).PET and clinical high-field (3-T) MRI were performed in list mode for up to 60 min after intravenous administration of 18 F-FET (mean 6 SD, 163 6 23 MBq; 180-MBq standard dose and individually calculated dose for body weight , 60 kg).Fasting for at least 4 h before PET acquisition was recommended.A gadolinium-based contrast agent (Gadovist; Bayer Pharma AG) was administered according to the patient's total body weight (0.1 mmol/kg).The MRI acquisition protocol included a transversal T1-weighted ultrashort echo time sequence for attenuation and scatter correction, a T2-weighted sequence (repetition time/echo time, 5,320/88 ms; matrix size, 230 3 230 3 230; voxel size, 0.4 3 0.4 3 3.0 mm), and a postcontrast T1-weighted magnetizationprepared rapid gradient echo sequence (repetition time/echo time/inversion time, 2,400/2.26/900ms; flip angle, 8 ; matrix size, 256 3 256 3 256; voxel size, 1.0 3 1.0 3 1.0 mm; thickness, 1 mm; slices, 192).Vendorbased attenuation correction (software versions MR B20P and MR E11P) using ultrashort echo time was performed.The PET acquisition was reconstructed into transaxial slices using an iterative orderedsubset expectation maximization algorithm (ordered-subsets expectation maximization, 3 iterations and 21 subsets; matrix size, 344 3 344 3 127; voxel size, 1.0 3 1.0 3 2.3 mm; gaussian filter, 3 mm).Emission data were corrected for decay, randoms, dead time, scatter, and attenuation.

PET and MRI Analysis
Quantitative analysis was performed using OsiriX MD 12 (Pixmeo SARL).Contrast-enhancing and T2-weighted lesion volumes were determined in an automated manner using an attention-based U-Net architecture with postcontrast T1-weighted magnetization-prepared rapid gradient echo and T2-weighted/fluid-attenuated inversion recovery images as input (11).When applicable, regions of interest (ROIs) of T2-weighted and contrast-enhancing lesion volumes were slightly adapted. 18F-FET uptake was measured in an automated manner using isocontouring based on attenuation-corrected 18 F-FET tracer uptake, yielding an uptake-based total (60%-100%, MTV 60 ), peripheral (60%-75%, ROI 60 ), and central (80%-100%, ROI 80 ) metabolic compartment (thresholds were iteratively determined in a pilot experiment, differentiating uptake pattern into a central and peripheral compartment based on visual assessment).Segmentations were marginally adapted to exclude large intracranial blood vessels (when applicable).For definition of metabolic tumor volume, both a percentage method (60%-100%, MTV 60 ) and an absolute threshold method (1.8 times the mean uptake of the healthy contralateral background, MTV) were used.CU ratio was defined as the fraction between ROI 60 and ROI 80 , yielding a volumetric CU ratio for the uptake-based volume or SUV CU ratio for the mean SUV.Mean target-to-background ratio was determined from SUV mean on the basis of a 3-dimensional VOI (ROI 80 ) from an 18 F-FET-active lesion compared with the mean unaffected contralateral background to account for nonspecific and regional uptake behavior.In multifocal tumor manifestations, the most prominent 18 F-FETactive lesion was chosen as the target lesion.Mean background tracer uptake was computed from a contralateral 2-dimensional ROI of similar size in unaffected brain tissue on a representative slice with the highest mean uptake within the tumor volume.Threedimensional ROIs from magnetization-prepared rapid gradient echo and 18 F-FET PET were then transformed to a structural T2-weighted image using rigid deformation (ANTs, version 2.3.4).

Statistics
Statistical analysis was performed using Prism version 9 (GraphPad Software) and MedCalc version 20.104 (MedCalc Software Ltd.).Mann-Whitney U (2-tailed) tests (with Holm-S ıd ak multiple-comparison testing) were used for comparisons between 2 groups.Wilcoxon signedrank (1-tailed) testing was used for matched pairs.Kruskal-Wallis testing (with Dunn multiple-comparison testing) was used for comparisons among 3 groups.Receiver-operating-characteristic analysis was performed using the DeLong method reporting area under the curve (AUC), 95% CIs, and P value.Sensitivity and specificity were reported for the best cutoff point independent of the prevalence determined using the Youden index.Logistic regression was used to model binary outcome (predictive accuracy based on a P value cutoff of 0.5).Measurements were correlated and evaluated using the nonparametric Spearman correlation coefficient (2-tailed).Intersections between volumes were computed on the basis of the Dice coefficient (Eq. 1) and the Jaccard index.In all tests, a P value of less than 0.05 was considered statistically significant.

DISCUSSION
We introduced a noninvasive metabolic imaging biomarker for the assessment of metabolic reprogramming in gliomas and demonstrated its diagnostic potential for the predictive genotyping of IDH mutation status by characterizing the spatially heterogeneous amino acid metabolism in a patient cohort with preoperative glioma.First, we showed that the MTV 60 in 18 F-FET PET is distinct from contrast-enhanced MRI, which is the clinical standard for the initial diagnosis, biopsy targeting, and surveillance of brain tumors.Furthermore, MTV 60 presented greater dimensions than contrast-enhanced MRI, which is known to underestimate tumor margins because of diffuse infiltration beyond areas of blood-brain barrier impairment (12,13).Exploiting the cancer amino acid metabolism, we proposed the volumetric CU ratio as a biologic determinant for the assessment of CU characteristics, which determined IDH mutation status in this cohort of treatment-naïve glioma patients with excellent diagnostic accuracy, suggesting a central role for noninvasive genotyping before surgical intervention.
Gene expression in glioma is known to be spatially distinct, for example, presenting differential upregulation of tyrosine aminotransferase-where increased expression in the tumor core as opposed to the periphery was reported-and a corresponding activation of the tyrosine metabolism (14).Because of this differential expression and metabolism, the IDH genotype, which is a critical regulator of both glucose and amino acid metabolism (15), was determined in an indirect bottom-up approach in the current study.Interestingly, LOH1p/19q, MGMT promoter methylation, and ATRX loss were differentiated by spatial metabolic patterns-although diagnostic performance was moderate-suggesting indirect associations.Similar to results from previous metabolic imaging studies (16)(17)(18), the averaged 18 F-FET uptake differentiated IDH genotype with modest diagnostic performanceinferior to the volumetric CU ratio.MTV 60 and MTV were not suitable for differentiation between IDH genotypes, but a weak correlation between MTV 60 and volumetric CU ratio was observed.CU characteristics may also be determined as a ratio of total (summed) uptake (instead of volume) or may be accessible from histogram analysis.Although IDH-mutated astrocytoma and oligodendroglioma are considered distinct tumor entities, both could be independently differentiated from IDH wild-type glioblastoma at the same threshold, further suggesting that the volumetric CU ratio reflects metabolic reprogramming dependent on the IDH genotype.We could not observe increased uptake in IDH-mutated oligodendroglioma compared with IDH-mutated astrocytoma, as suggested in previous studies (17,19)-likely because of the relatively low number of these tumors in the cohort.When metabolic compartments were compared between IDH-mutated and IDH wildtype glioma, only the peripheral compartment was increased in IDH-mutated tumors, which corresponded to a visually apparent heterogeneous uptake observed in some tumors.
as well as the propensity to artifacts, particularly in the posttreatment setting.Moreover, D-2-hydroxyglutarate MR spectroscopy requires off-line postprocessing, currently impeding more widespread implementation.Of note is that single-voxel MR spectroscopy depends on the accuracy of the voxel placement in often heterogeneous tumors.Diffusion MRI was shown to correspond to the IDH genotype in CNS WHO 2 and 3 gliomas (27); however, modest diagnostic power for the differentiation of IDH mutation status was apparent using both single-and multiple-shell imaging.A study by Lohmann et al. ( 28) using 18 F-FET PET radiomics suggested that the combined biparametric analysis of conventional uptake parameters with additional textural features can achieve a similar diagnostic accuracy (as reported here); nonetheless, textural feature analysis is a complex and time-demanding approach, which suffers from well-known issues of restricted generalizability, overfitting, or other methodologic flaws (29).Experimental advanced imaging techniques, such as chemical exchange saturation transfer (30) or hyperpolarized 13 C-MRI (31), were also demonstrated to show correspondence to the IDH genotype, but clinical implementation is currently challenging.In contrast, the current study's approach demonstrated robust IDH classification based on static 18 F-FET PET without the need for complex analysis.

Clinical Relevance of Biomarker-Driven IDH Classification
The IDH genotype is a clinically important marker for molecular targeting, surgical planning, and individual prognosis (5).With the paradigm shift to molecular markers in clinical management of CNS cancer, there is a great clinically unmet need for noninvasive biomarker-driven classification.In clinical settings where limited tissue specimens are obtained, such as in stereotactic minimally invasive MRI-guided laser ablation or laser interstitial thermal therapy (32), an additional clinical benefit is expected.Molecular stratification before surgical intervention provides opportunities for more effective individualized neoadjuvant therapeutic strategies, an important topic in multimodal cytoreductive therapy because IDH-mutated gliomas are associated with better outcomes from radiochemotherapy (2,3).Furthermore, an imaging biomarker-driven classification aids the identification of patients with an increased risk of recurrence, allowing for earlier and more aggressive treatment regimens.

Limitations
General conclusions should be drawn with caution because of the retrospective nature of this study.Further research with larger multicenter cohorts (with different scanners and reconstruction settings) and a prospective study design is required.The CU ratio using isocontouring may be sensitive to spatial image resolution and PET scanner variability.Subsequent investigations should evaluate the impact of different spatial resolutions and PET scanners, incorporating harmonization techniques (e.g., scanner-specific calibration phantoms or image postprocessing methods) and sensitivity analyses, thereby improving generalizability and clinical applicability.
Findings from the current study are restricted to 18 F-FET-active tumors (76% of confirmed preoperative glioma with available imaging in the current study).In PET photopenic CNS cancer, advanced imaging techniques, such as MR spectroscopy, chemical exchange saturation transfer, or diffusion MRI, would certainly provide supplementary information, which should also be investigated in multimodal and multiparametric research for IDH genotyping-with potential to obviate a preceding biopsy.Furthermore, diffuse and multifocal tumor manifestations may result in divergent metabolic and molecular signatures.The current cohort comprised a mixed patient population, which is reflective of the situation in clinical practice.A study population with the same histologic subtype could provide greater comparability at the cost of restricted generalizability; nonetheless, our results suggest that the CU ratio reflects metabolic reprogramming independent of tumor entity.Of particular note is that former IDH-mutated glioblastomas are classified as astrocytoma CNS WHO grade 4 and that oligodendrogliomas are genetically defined by IDH mutation and LOH1p/19q according to the 2021 WHO classification (1).Although the determined isocontouring thresholds achieve plausible segmentation into central and peripheral metabolic compartments, an immunohistochemical correlation and further optimization of thresholds based on tissue specimens merit further research.Future studies should investigate response assessment of IDH-targeted therapy, as well as CU characteristics in other IDH mutation-associated tumors, including acute myeloid leukemia, cholangiocarcinoma, or chondrosarcoma.Multilateral interactions between cancer metabolism, oncogenic pathways, and the tumor microenvironment, particularly interactions between cancer, immune, and neuronal cells, are further areas for future studies.

CONCLUSION
The IDH genotype has a significant impact on the diagnosis and treatment of glioma.We proposed parametric 18 F-FET PET as a noninvasive metabolic biomarker for the classification of IDH genotype-with critical implications for clinical management and the diagnostic workup of patients with CNS cancer.

KEY POINTS
QUESTION: Is the IDH genotype-a critical regulator of glucose and amino acid metabolism-associated with a distinct metabolic phenotype in amino acid PET?
PERTINENT FINDINGS: Fifty-two patients with preoperative glioma were retrospectively investigated using static 18 F-FET PET.Metabolic tumor volume was distinct and presented greater dimensions than contrast enhancement, which is known to underestimate tumor margins.Evaluation of compartmental 18 F-FET uptake characteristics determined IDH genotype with excellent diagnostic performance, establishing a critical association between spatial metabolic heterogeneity, mitochondrial tricarboxylic acid cycle, and genomic features.Molecular classification of LOH1p/19q, MGMT promoter methylation, and ATRX loss by spatial metabolic patterns was possible, suggesting indirect associations with tyrosine metabolism.

IMPLICATIONS FOR PATIENT CARE:
We proposed parametric 18 F-FET PET as a noninvasive metabolic biomarker for the classification of IDH genotype before surgical intervention, with implications for clinical management and the diagnostic workup of patients with glioma.

TABLE 1
Characteristics of Patient Cohort Data are number, except for age.

TABLE 2
Diagnostic Measures from18F-FET PET for IDH Classification